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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Perspective

The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD)

Author(s): Igor José dos Santos Nascimento*, Thiago Mendonça de Aquino and Edeildo Ferreira da Silva-Júnior

Volume 19, Issue 11, 2022

Published on: 31 May, 2022

Page: [951 - 955] Pages: 5

DOI: 10.2174/1570180819666220405225817

Abstract

Drug design and discovery is a process that requires high financial costs and is timeconsuming. For many years, this process focused on empirical pharmacology. However, over the years, the target-based approach allowed a significant discovery in this field, initiating the rational design era. In view, to decrease the time and financial cost, rational drug design is benefited by increasing computer engineering and software development, and computer-aided drug design (CADD) emerges as a promising alternative. Since the 1970s, this approach has been able to identify many important and revolutionary compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified through this approach have shown their importance, being CADD essential in any drug discovery campaign. Thus, this perspective will present the prominent successful cases utilizing this approach and entering into the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics, using high-performance computing with molecular dynamics protocols faster and more effectively. In addition, artificial intelligence and machine learning will be the next process in the rational design of new drugs. Here, we hope that this paper generates new ideas and instigates research groups worldwide to use these methods and stimulate progress in drug design.

Keywords: Drug design, drug discovery, molecular modeling, computer-aided drug design, in silico methods, bioinformatics.

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[1]
Brown, N.; Ertl, P.; Lewis, R.; Luksch, T.; Reker, D.; Schneider, N. Artificial intelligence in chemistry and drug design. J. Comput. Aided Mol. Des., 2020, 34(7), 709-715.
[http://dx.doi.org/10.1007/s10822-020-00317-x] [PMID: 32468207]
[2]
Dos Santos Nascimento, I.J.; de Aquino, T.M.; da Silva-Júnior, E.F. Cruzain and rhodesain inhibitors: Last decade of advances in seeking for new compounds against American and African trypanosomiases. Curr. Top. Med. Chem., 2021, 21(21), 1871-1899.
[http://dx.doi.org/10.2174/1568026621666210331152702] [PMID: 33797369]
[3]
Nicolaou, K.C. Advancing the drug discovery and development process. Angew. Chem. Int. Ed. Engl., 2014, 53(35), 9128-9140.
[http://dx.doi.org/10.1002/anie.201404761] [PMID: 25045053]
[4]
Wang, L.; Ding, J.; Pan, L.; Cao, D.; Jiang, H.; Ding, X. Artificial intelligence facilitates drug design in the big data era. Chemom. Intell. Lab. Syst., 2019, 194, 103850.
[http://dx.doi.org/10.1016/j.chemolab.2019.103850]
[5]
Deshaies, R.J. Multispecific drugs herald a new era of biopharmaceutical innovation. Nature, 2020, 580(7803), 329-338.
[http://dx.doi.org/10.1038/s41586-020-2168-1] [PMID: 32296187]
[6]
Poroikov, V.V. Computer-aided drug design: From discovery of novel pharmaceutical agents to systems pharmacology. Biochem. (Moscow), Suppl. Ser. B Biomed. Chem, 2020, 14, 216-227.
[7]
de Aquino, T.M.; França, P.H.B.; Rodrigues, É.E.E.S.; Nascimento, I.J.S.; Santos-Júnior, P.F.S.; Aquino, P.G.V.; Santos, M.S.; Queiroz, A.C.; Araújo, M.V.; Alexandre-Moreira, M.S.; Rodrigues, R.R.L.; Rodrigues, K.A.F.; Freitas, J.D.; Bricard, J.; Meneghetti, M.R.; Bourguignon, J-J.; Schmitt, M.; da Silva-Júnior, E.F.; de Araújo-Júnior, J.X. Synthesis, antileishmanial activity and in silico studies of aminoguanidine hydrazones (AGH) and thiosemicarbazones (TSC) against leishmania chagasi amastigotes. Med. Chem. (Los Angeles), 2022, 18(2), 151-169.
[8]
Berdigaliyev, N.; Aljofan, M. An overview of drug discovery and development. Future Med. Chem., 2020, 12(10), 939-947.
[http://dx.doi.org/10.4155/fmc-2019-0307] [PMID: 32270704]
[9]
Schneider, P.; Walters, W.P.; Plowright, A.T.; Sieroka, N.; Listgarten, J.; Goodnow, R.A., Jr; Fisher, J.; Jansen, J.M.; Duca, J.S.; Rush, T.S.; Zentgraf, M.; Hill, J.E.; Krutoholow, E.; Kohler, M.; Blaney, J.; Funatsu, K.; Luebkemann, C.; Schneider, G. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov., 2020, 19(5), 353-364.
[http://dx.doi.org/10.1038/s41573-019-0050-3] [PMID: 31801986]
[10]
José dos Santos Nascimento, I.; Mendonça de Aquino, T.; Fernando da Silva Santos-Júnior, P.; Xavier de Araújo-Júnior, J.; Ferreira da Silva-Júnior, E. Molecular modeling applied to design of cysteine protease inhibitors – a powerful tool for the identification of hit compounds against neglected tropical diseases. Front. Comput. Chem., 2020, 5, 63-110.
[11]
Cui, W.; Aouidate, A.; Wang, S.; Yu, Q.; Li, Y.; Yuan, S. Discovering anti-cancer drugs via computational methods. Front. Pharmacol., 2020, 11, 733.
[http://dx.doi.org/10.3389/fphar.2020.00733] [PMID: 32508653]
[12]
Dos Santos Nascimento, I.J.; de Aquino, T.M.; da Silva-Júnior, E.F. Drug repurposing: A strategy for discovering inhibitors against emerging viral infections. Curr. Med. Chem., 2021, 28(15), 2887-2942.
[http://dx.doi.org/10.2174/0929867327666200812215852] [PMID: 32787752]
[13]
Yu, W.; MacKerell, A.D. Computer-aided drug design methods. Methods Mol. Biol., 2017, 1520, 85-106.
[http://dx.doi.org/10.1007/978-1-4939-6634-9_5]
[14]
Salman, M.M.; Al-Obaidi, Z.; Kitchen, P.; Loreto, A.; Bill, R.M.; Wade-Martins, R. Advances in applying computer-aided drug design for neurodegenerative diseases. Int. J. Mol. Sci., 2021, 22(9), 4688.
[http://dx.doi.org/10.3390/ijms22094688] [PMID: 33925236]
[15]
Brogi, S.; Ramalho, T.C.; Kuca, K.; Medina-Franco, J.L.; Valko, M. Editorial: In silico methods for drug design and discovery. Front Chem., 2020. Available from:
[http://dx.doi.org/10.3389/fchem.2020.00612]
[16]
Njogu, P.M.; Guantai, E.M.; Pavadai, E.; Chibale, K. Computer-aided drug discovery approaches against the tropical infectious diseases malaria, tuberculosis, trypanosomiasis, and leishmaniasis. ACS Infect. Dis., 2016, 2(1), 8-31.
[http://dx.doi.org/10.1021/acsinfecdis.5b00093] [PMID: 27622945]
[17]
Surabhi, S.; Singh, B. Computer aided drug design: An overview. J. Drug Deliv. Ther., 2018, 8(5), 504-509.
[http://dx.doi.org/10.22270/jddt.v8i5.1894]
[18]
Silva, L.R.; Guimarães, A.S.; do Nascimento, J.; do Santos Nascimento, I.J.; da Silva, E.B.; McKerrow, J.H.; Cardoso, S.H.; da Silva-Júnior, E.F. Computer-aided design of 1,4-naphthoquinone-based inhibitors targeting cruzain and rhodesain cysteine proteases. Bioorg. Med. Chem., 2021, 41, 116213.
[http://dx.doi.org/10.1016/j.bmc.2021.116213] [PMID: 33992862]
[19]
Song, C.M.; Lim, S.J.; Tong, J.C. Recent advances in computer-aided drug design. Brief. Bioinform., 2009, 10(5), 579-591.
[http://dx.doi.org/10.1093/bib/bbp023] [PMID: 19433475]
[20]
Leelananda, S.P.; Lindert, S. Computational methods in drug discovery. Beilstein J. Org. Chem., 2016, 12, 2694-2718.
[http://dx.doi.org/10.3762/bjoc.12.267] [PMID: 28144341]
[21]
Kalyaanamoorthy, S.; Chen, Y.P.P. Structure-based drug design to augment hit discovery. Drug Discov. Today, 2011, 16(17-18), 831-839.
[http://dx.doi.org/10.1016/j.drudis.2011.07.006] [PMID: 21810482]
[22]
Ghosh, A.K.; Gemma, S. Carbonic anhydrase inhibitors for the treatment of glaucoma: Design and discovery of dorzolamide. In: Structure-Based Design of Drugs and Other Bioactive Molecules; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2015; pp. 411-419.
[23]
Hayden, F.G. Antivirals for influenza: Historical perspectives and lessons learned. Antiviral Res., 2006, 71(2-3), 372-378.
[http://dx.doi.org/10.1016/j.antiviral.2006.05.016] [PMID: 16815563]
[24]
Farina, V.; Brown, J.D. Tamiflu: The supply problem. Angew. Chem. Int. Ed., 2006, 45(44), 7330-7334.
[http://dx.doi.org/10.1002/anie.200602623] [PMID: 17051628]
[25]
Salsbury, F.R., Jr Molecular dynamics simulations of protein dynamics and their relevance to drug discovery. Curr. Opin. Pharmacol., 2010, 10(6), 738-744.
[http://dx.doi.org/10.1016/j.coph.2010.09.016] [PMID: 20971684]
[26]
Furet, P.; Guagnano, V.; Fairhurst, R.A.; Imbach-Weese, P.; Bruce, I.; Knapp, M.; Fritsch, C.; Blasco, F.; Blanz, J.; Aichholz, R.; Hamon, J.; Fabbro, D.; Caravatti, G. Discovery of NVP-BYL719 a potent and selective phosphatidylinositol-3 kinase alpha inhibitor selected for clinical evaluation. Bioorg. Med. Chem. Lett., 2013, 23(13), 3741-3748.
[http://dx.doi.org/10.1016/j.bmcl.2013.05.007] [PMID: 23726034]
[27]
André, F.; Ciruelos, E.; Rubovszky, G.; Campone, M.; Loibl, S.; Rugo, H.S.; Iwata, H.; Conte, P.; Mayer, I.A.; Kaufman, B.; Yamashita, T.; Lu, Y-S.; Inoue, K.; Takahashi, M.; Pápai, Z.; Longin, A-S.; Mills, D.; Wilke, C.; Hirawat, S.; Juric, D. Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced breast cancer. N. Engl. J. Med., 2019, 380(20), 1929-1940.
[http://dx.doi.org/10.1056/NEJMoa1813904] [PMID: 31091374]
[28]
Tanaka, N.; Lin, J.J.; Li, C.; Ryan, M.B.; Zhang, J.; Kiedrowski, L.A.; Michel, A.G.; Syed, M.U.; Fella, K.A.; Sakhi, M.; Baiev, I.; Juric, D.; Gainor, J.F.; Klempner, S.J.; Lennerz, J.K.; Siravegna, G.; Bar-Peled, L.; Hata, A.N.; Heist, R.S.; Corcoran, R.B. Clinical acquired resistance to KRASG12C inhibition through a novel KRAS switch-II pocket mutation and polyclonal alterations converging on RAS-MAPK reactivation. Cancer Discov., 2021, 11(8), 1913-1922.
[http://dx.doi.org/10.1158/2159-8290.CD-21-0365]
[29]
Canon, J.; Rex, K.; Saiki, A.Y.; Mohr, C.; Cooke, K.; Bagal, D.; Gaida, K.; Holt, T.; Knutson, C.G.; Koppada, N.; Lanman, B.A.; Werner, J.; Rapaport, A.S.; San Miguel, T.; Ortiz, R.; Osgood, T.; Sun, J-R.; Zhu, X.; McCarter, J.D.; Volak, L.P.; Houk, B.E.; Fakih, M.G.; O’Neil, B.H.; Price, T.J.; Falchook, G.S.; Desai, J.; Kuo, J.; Govindan, R.; Hong, D.S.; Ouyang, W.; Henary, H.; Arvedson, T.; Cee, V.J.; Lipford, J.R. The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature, 2019, 575(7781), 217-223.
[http://dx.doi.org/10.1038/s41586-019-1694-1] [PMID: 31666701]
[30]
Doweyko, A.M. QSAR: Dead or alive? J. Comput. Aided Mol. Des., 2008, 22(2), 81-89.
[http://dx.doi.org/10.1007/s10822-007-9162-7] [PMID: 18189157]
[31]
Mohamed, K.; Yazdanpanah, N.; Saghazadeh, A.; Rezaei, N. Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review. Bioorg. Chem., 2021, 106, 104490.
[http://dx.doi.org/10.1016/j.bioorg.2020.104490] [PMID: 33261845]
[32]
Fernando da Silva Santos-Junior, P.; Jose dos Santos Nascimento, I.; Mendonca de Aquino, T.; Xavier de Araujo-Junior, J.; Ferreira da Silva-Junior, E. Drug discovery strategies against emerging coronaviruses: A global threat. Front. Anti-Infect. Drug Discov., 2020, 8, 35-90.
[http://dx.doi.org/10.2174/9789811412387120080004]
[33]
Daina, A.; Röhrig, U.F.; Zoete, V. Computer-aided drug design for cancer therapy. In: Systems Medicine; Elsevier, 2021; pp. 386-401.
[http://dx.doi.org/10.1016/B978-0-12-801238-3.11670-8]
[34]
Jorgensen, W.L. Challenges for academic drug discovery. Angew. Chem. Int. Ed. Engl., 2012, 51(47), 11680-11684.
[http://dx.doi.org/10.1002/anie.201204625] [PMID: 23097176]
[35]
Taft, C.A.; Da Silva, V.B.; Da Silva, C.H.T. Current topics in computer-aided drug design. J. Pharm. Sci., 2008, 97(3), 1089-1098.
[http://dx.doi.org/10.1002/jps.21293] [PMID: 18214973]
[36]
Mitchell, W.; Matsumoto, S. Large-scale integrated super-computing platform for next generation virtual drug discovery. Curr. Opin. Chem. Biol., 2011, 15(4), 553-559.
[http://dx.doi.org/10.1016/j.cbpa.2011.06.005] [PMID: 21723773]
[37]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2013, 66(1), 334-395.
[http://dx.doi.org/10.1124/pr.112.007336] [PMID: 24381236]
[38]
Faver, J.C.; Ucisik, M.N.; Yang, W.; Merz, K.M. Jr Computer-aided drug design: Using numbers to your advantage. ACS Med. Chem. Lett., 2013, 4(9), 812-814.
[http://dx.doi.org/10.1021/ml4002634] [PMID: 24312700]
[39]
Ge, H.; Wang, Y.; Li, C.; Chen, N.; Xie, Y.; Xu, M.; He, Y.; Gu, X.; Wu, R.; Gu, Q.; Zeng, L.; Xu, J. Molecular dynamics-based virtual screening: Accelerating the drug discovery process by high-performance computing. J. Chem. Inf. Model., 2013, 53(10), 2757-2764.
[http://dx.doi.org/10.1021/ci400391s] [PMID: 24001302]
[40]
Sou, T.; Hansen, J.; Liepinsh, E.; Backlund, M.; Ercan, O.; Grinberga, S.; Cao, S.; Giachou, P.; Petersson, A.; Tomczak, M.; Urbas, M.; Zabicka, D.; Vingsbo Lundberg, C.; Hughes, D.; Hobbie, S.N.; Friberg, L.E. Model-informed drug development for antimicrobials: Translational PK and PK/PD modeling to predict an efficacious human dose for apramycin. Clin. Pharmacol. Ther., 2021, 109(4), 1063-1073.
[http://dx.doi.org/10.1002/cpt.2104] [PMID: 33150591]
[41]
van Os, W.; Zeitlinger, M. Predicting antimicrobial activity at the target site: Pharmacokinetic/pharmacodynamic indices versus time-kill approaches. Antibiotics (Basel), 2021, 10(12), 1485.
[http://dx.doi.org/10.3390/antibiotics10121485] [PMID: 34943697]
[42]
Diao, L.; Meibohm, B. Tools for predicting the PK/PD of therapeutic proteins. Expert Opin. Drug Metab. Toxicol., 2015, 11(7), 1115-1125.
[http://dx.doi.org/10.1517/17425255.2015.1041917] [PMID: 25936400]
[43]
de Lorenzo, V. Systems biology approaches to bioremediation. Curr. Opin. Biotechnol., 2008, 19(6), 579-589.
[http://dx.doi.org/10.1016/j.copbio.2008.10.004] [PMID: 19000761]
[44]
Zhu, J.; Zhang, B.; Schadt, E.E. A systems biology approach to drug discovery. Adv. Genet., 2008, 60, 603-635.
[http://dx.doi.org/10.1016/S0065-2660(07)00421-X]
[45]
Kohl, P.; Crampin, E.J.; Quinn, T.A.; Noble, D. Systems biology: An approach. Clin. Pharmacol. Ther., 2010, 88(1), 25-33.
[http://dx.doi.org/10.1038/clpt.2010.92] [PMID: 20531468]
[46]
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]

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